EKF based speed sensorless vector control of an induction machine

Khafallah, Mohamed; Afia, Abdellatif El; Chériti, A.; Mariami, Faissal El; Saâd, Abdallah; Moussaoui, B. El

doi:10.1109/icit.2004.1490755

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…As one of the strategies in sensorless vector control of induction motor, the EKF has the features of following in [2]: (a) EKF can work perfectly in the environment filled with noise. The estimation will be very accurate even with the noise disturbance; (b) there are many parameters to be tuned before EKF can run.…”

Section: ) Estimator Based On Artificial Neural Network (Ann)mentioning

confidence: 99%

Research on a Modified EKF for Speed Estimation in Induction Motor Drives

Liu

fan

2007

2007 IEEE International Conference on Integration Technology

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This paper presents and proposes a new approach to resolve the problems of large operation algorithmΔvery short sample time and redundant parameters tuning in induction motor sensorless vector control. The estimation method is based on a long sample time, reduced-order EKF. With this model structure, only the rotor flux components are estimated, besides the rotor speed itself. The new methods predict the rotor speed in the every sample time. So it does not need a short sample time to assume that the differential of speed to be zero. Simulation results show that this new approach can enable us to reduce the execution time of the algorithm without difficulties related to the tuning of covariance matrices and achieve robust speed estimation even in the condition of long sample time.

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Section: ) Estimator Based On Artificial Neural Network (Ann)mentioning

confidence: 99%

Research on a Modified EKF for Speed Estimation in Induction Motor Drives

Liu

fan

2007

2007 IEEE International Conference on Integration Technology

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Filtering of discrete-time for noise in coefficient matrix based on H<inf>∞</inf> control

Man

Zhang

2011

2011 Chinese Control and Decision Conference (CCDC)

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Extended Kalman Filter for characterizing a wind energy conversion system based onvariable speed permanent magnet synchronous generator

Mesbahi

Raihani

Bouattane

et al. 2014

2014 International Renewable and Sustainable Energy Conference (IRSEC)

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This paper presents an approach to determine the power coefficient for a standalone wind energy conversion system (WECS) and estimate the wind speed. The knowledge of the power coefficient requires absolutely estimating the mechanical power supplied by the wind turbine of the system. An Extended Kalman Filter (EKF) associated to a PMSG model is used to reconstruct angular velocity and position. Generally the WECS manufacturer provides the power output characteristic. Hence, the wind speed and power coefficient can be deduced by knowing the rotor angular speed. All the used measurement points are stored as a database to allow a deep study and a good estimation and knowledge of the system.

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EKF based speed sensorless vector control of an induction machine

Cited by 3 publications

References 7 publications

Research on a Modified EKF for Speed Estimation in Induction Motor Drives

Research on a Modified EKF for Speed Estimation in Induction Motor Drives

Filtering of discrete-time for noise in coefficient matrix based on H<inf>∞</inf> control

Extended Kalman Filter for characterizing a wind energy conversion system based onvariable speed permanent magnet synchronous generator

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EKF based speed sensorless vector control of an induction machine

Cited by 3 publications

References 7 publications

Research on a Modified EKF for Speed Estimation in Induction Motor Drives

Research on a Modified EKF for Speed Estimation in Induction Motor Drives

Filtering of discrete-time for noise in coefficient matrix based on H<inf>&#x221E;</inf> control

Extended Kalman Filter for characterizing a wind energy conversion system based onvariable speed permanent magnet synchronous generator

Contact Info

Product

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Filtering of discrete-time for noise in coefficient matrix based on H<inf>∞</inf> control